基于不同骨架的语义分割网络的建筑物提取

Building Extraction Based on Semantic Segmentation Networks with Different Skeletons

采用高分辨率遥感影像进行建筑物提取,会出现提取的建筑物边缘线条缺失和错提问题,采用骨架代替编码器卷积层,可以在一定程度上解决这些问题。文章采用三种不同的骨架对DeeplabV3+和UNet深度学习网络进行改进。用WHU和Inria数据集进行验证,结果表明:引入三种骨架后的改进网络相对于无权重DeeplabV3+,在WHU数据集上精度分别提高了0.49%、1.52%和0.87%;UNet网络精度分别提高了1.15%、3.24%和3.13%。在Inria数据上可以得到同样的结论,在一定程度上解决了边线缺失和漏提问题。

Using high-resolution remote sensing images for building extraction may result in missing and incorrect extraction of building edge lines.Replacing encoder convolutional layers with skeletons can solve these problems to some extent.This paper uses three different skeletons to improve DeeplabV3+and UNet Deep Learning networks.Using the WHU and Inria datasets for verification,the results show that the improved network with the introduction of three skeletons improves accuracy by 0.49%,1.52%,and 0.87%compared to DeeplabV3+on the WHU dataset,respectively.The accuracy of UNet network improves by 1.15%,3.24%,and 3.13%,respectively.The same conclusion can be drawn on the Inria data,which solves the problems of missing and miss extraction of edge lines to some extent.